Graphical Exploration of a Database

ABSTRACT

Techniques implemented in methods, computer-readable media apparatus, and systems for graphically exploring an in-memory database include: identifying a selected data object; searching an in-memory database for a data element associated with a search term; and determining a relationship between the data element and the selected data object; and generating a displayable graph of the relationship as a path between the data element and the selected data object, the path comprising at least two nodes, where a first node represents the selected data object and a second node represents a node associated with the data element.

TECHNICAL BACKGROUND

This disclosure relates to providing graphical exploration of adatabase, such as an in-memory database.

BACKGROUND

Business users of software in a business enterprise may utilize avirtual workspace to browse, view, modify, and/or otherwise manipulatedata related to the business enterprise. Such data may include a varietyof information in many different forms, such as sales data, revenuedata, human resources information, business hierarchy information, andotherwise. Graphs, tables, charts, electronic communications, webservices, reports, and other forms of data, may be viewable in theuser's workspace. The workspace may allow or facilitate the resolutionof business issues and/or problems by the user. The business data may bepresented to a user in a particular view, or data cube, allowing theuser to analyze the business data with respect to different contexts,such as a particular business issue, a product or service, anorganizational unit, or other relevant contexts. Each data cube may bedefined at design time prior to analysis of a data cube by a user.Further, defining a particular data cube may require knowledge of theunderlying structure of business data to be represented in the data cubeand the dimensions of the data cube that are to be joined.

SUMMARY

One general embodiment according to the present disclosure includestechniques implemented in methods, computer-readable media apparatus,and systems for graphically exploring an in-memory database include:identifying a selected data object; searching an in-memory database fora data element associated with a search term; and determining arelationship between the data element and the selected data object; andgenerating a displayable graph of the relationship as a path between thedata element and the selected data object, the path comprising at leasttwo nodes, where a first node represents the selected data object and asecond node represents a node associated with the data element.

A first aspect combinable with the general embodiment includes receivinga selection of a particular node on the path; and generating a new dataobject based on the selection.

In a second aspect combinable with any of the previous aspects, the newdata object includes a view of data in the in-memory database differentfrom a view associated with the selected data object.

In a third aspect combinable with any of the previous aspects, the newdata object includes the particular node on the path.

In a fourth aspect combinable with any of the previous aspects, aplurality of data objects associated with the search term are identifiedin response to searching the in-memory database.

In a fifth aspect combinable with any of the previous aspects, aplurality of relationships are determined between the selected dataobject and the plurality of data objects.

In a sixth aspect combinable with any of the previous aspects, each ofthe plurality of relationships is displayed in at least one of aplurality of paths, each of the plurality of paths sharing the selecteddata object as a common node.

In a seventh aspect combinable with any of the previous aspects, thesearch term is not associated with a dimension of the selected dataobject.

In an eighth aspect combinable with any of the previous aspects, the atleast one path further includes a third node representing an interveningnode between the first and second nodes.

In a ninth aspect combinable with any of the previous aspects, the thirdnode represents a data object associated with the data element, and thedata object is different from the selected data object.

In a tenth aspect combinable with any of the previous aspects, theselected data object is a data cube.

Various embodiments of providing graphical exploration of an in-memorydatabase according to the present disclosure may have one or more of thefollowing features. For example, the displayed paths representingrelationships between a selected data cube and data objects returned ina search of an in-memory database may allow a user to identifyparticular nodes or data to include in a new data cube. Accordingly, aselection of a particular node on the path is received, and a new datacube is generated based on the selection. The new data cube may includea view of data in the in-memory database different from a viewassociated with the selected data cube. The new data cube may alsoinclude the particular node on the path. Searching the in-memorydatabase may return a plurality of data objects associated with thesearch term. A plurality of relationships may be determined between theselected data cube and the plurality of data objects, wherein each ofthe plurality of relationships is displayed in at least one of aplurality of paths, each of the plurality of paths sharing the selecteddata cube as a common node. Further, the search term may not beassociated with a dimension of the selected data cube. The at least onepath further includes a third node representing an intervening nodebetween the first and second nodes. The third node represents a dataobject associated with the data element, the data object different fromthe selected data object.

These general and specific aspects may be implemented using a device,system or method, or any combinations of devices, systems, or methods.The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other features,objects, and advantages will be apparent from the description anddrawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 illustrates an example distributed computing system operable toprovide graphical exploration of an in-memory database;

FIGS. 2A-2B illustrate additional example distributed computing systemsoperable to generate, view, modify, and/or otherwise provide graphicalexploration of an in-memory database;

FIG. 3 illustrates an example virtual distributed computing systemoperable to generate, view, modify, and/or otherwise manipulate acontextual workspace;

FIGS. 4A-4D illustrate example screenshots of providing graphicalexploration of an in-memory database;

FIG. 5 illustrates an example method for providing graphical explorationof an in-memory database; and

FIG. 6 illustrates an example algorithm 600 for calculating a new datacube based on a predefined dimension.

DETAILED DESCRIPTION

FIG. 1 illustrates an example distributed computing system 100 operableto facilitate graphical exploration of an in-memory database. Dataobjects in the in-memory database may be comprised of business datarepresented in a particular view, or data cube. Alternatively, thebusiness data may be stored in and/or referenced by other data objects,such as, for example, relational databases, reports, documents, tables,and other objects or repositories. Although the examples below describeinteraction with data cubes, other types of data objects are also withinthe scope of the present disclosure. The data cube represents a dataobject defining a predefined or pre-calculated view on business data.Accordingly, the data cube can be referred to as a calculated view. Asexplained more fully below, the business data may be presented to theuser in any of a variety of techniques and views, such as, for example,reports, tables, notes, graphs, and other views. The business data maybe connected and/or related according to any of a variety of contexts,such as, for example, a particular business issue or problem, aparticular product or service, a particular organizational unit and/orportion of a business enterprise, across a particular position and/oruser role found within the business enterprise, and other contexts.

Typically, data cubes are defined during design time, prior to executionof queries for business data associated with the data cubes. Graphicalexploration of an in-memory database, however, may allow creation of newdata cubes in real-time in addition to the predefined data cubes. Thenew data cubes may be created based on generating a data structure graphof the underlying business data and allowing selection of specific datacubes or nodes in the data structure graph. Queries may be executed ondata in an in-memory database. The search results may return dataobjects associated with other data cubes or nodes. The other data cubesand nodes are linked to an initial data cube, and the relationshipbetween data cubes may be represented in the data structure graph. Auser may then select specific data objects, data cubes, or nodes to beincluded in a new data cube. In some instances, technical experts arenot needed to define the new data cubes, as is usually required for atraditional online analytical processing (OLAP) system.

The illustrated computing environment 100 includes a server system 105,a client system 110, and a remote computing system 130 communicablycoupled through a network 120. Although illustrated as single systems,each of the systems 105, 110, and 130 may include more than one systemand/or more than one computing device (e.g., computer, laptop, server,mobile device, and otherwise) within a distributed computingenvironment. In general, computing environment 100 depicts an exampleconfiguration of a system capable of providing stateful execution ofstateless applications in a substantially transparent way, as well asdynamically determining the particular application's mode of operationin response to requests from its clients (e.g., client appliances 125).

The illustrated server system 105 includes one or more server appliances115 having corresponding graphical user interfaces (GUIs) 117. Ingeneral, the server appliance 115 is a server that stores one or moreapplications, where at least a portion of the applications are executedvia requests and responses sent to users or clients within andcommunicably coupled to the illustrated environment 100 of FIG. 1. Insome instances, the server appliance 115 may store a plurality ofvarious hosted applications, while in other instances, the serverappliance 115 may be a dedicated server meant to store and execute onlya single hosted application (e.g., the contextual workspace (server) 160or the graphical exploration module 180). In some instances, the serverappliance 115 may comprise a web server, where the applications, such asthe graphical exploration module 180, represent one or more web-basedapplications accessed and executed via network 120 by the clientappliances 125 of the environment 100 to perform the programmed tasks oroperations of the hosted applications.

At a high level, the server appliance 115 comprises an electroniccomputing device operable to receive, transmit, process, store, ormanage data and information associated with the environment 100.Specifically, the server appliance 115 illustrated in FIG. 1 isresponsible for receiving application requests from one or moreapplications (e.g., associated with the clients 125 of environment 100)and responding to the received requests by processing said requests inthe associated hosted application 114, and sending the appropriateresponse from the hosted application 114 back to the requesting clientapplication 144. In addition to requests from the external clients 125illustrated in FIG. 1, requests associated with the hosted applications114 may also be sent from internal users, external or third-partycustomers, other automated applications, as well as any otherappropriate entities, individuals, systems, or computers.

As used in the present disclosure, the term “computer” is intended toencompass any suitable processing device. For example, although FIG. 1illustrates a single server appliance 115, environment 100 can beimplemented using two or more server appliances 115, as well ascomputers other than servers, including a server pool. Indeed, serverappliance 115 may be any computer or processing device such as, forexample, a blade server, general-purpose personal computer (PC),Macintosh, workstation, UNIX-based workstation, or any other suitabledevice. In other words, the present disclosure contemplates computersother than general purpose computers, as well as computers withoutconventional operating systems. Further, illustrated server appliance115 may be adapted to execute any operating system, including Linux,UNIX, Windows, Mac OS, or any other suitable operating system.

The illustrated server appliance 115 is communicably coupled within-memory database 140 in the server system 105. In some embodiments,server 115 and/or certain of its components may be integrated withdatabase 140 so that, for instance, processing (e.g., all or partial)may be performed directly on in-memory data with processing resultspassed (e.g., via a communication channel) directly to a client. Inalternative embodiments, the in-memory database 140 may be locatedexternal to the server system 105 and communicably coupled to one ormore of the server system 105 and/or client system 110 through thenetwork 120. The illustrated in-memory database 140 may includeintegrated processing, i.e., all business and/or analytic operationsdone in processing memory. Moreover, content from business contentsources (described more fully below) may be replicated from one or moretransactional systems (e.g., coupled to the network 120) to thein-memory database 140 immediately. Thus, the in-memory database 140, insome aspects, may handle the analytical systems for all business data inreal-time, as opposed to, for instance, computational processing systemsthat have separate transactional and analytical systems that connectthrough relational databases (i.e., relational databases stored onmagnetic memory that require a process, e.g., ETL, to transfer data fromone system to another not in real time but with a delay of an hour, day,week, or longer).

In some embodiments, the in-memory database 140 may expose business dataand capabilities to improve an end-solution for end users (e.g., theclient appliances 125). The in-memory database 140 may reside on top ofa computational engine (e.g., in the server appliance 115 or otherwise)that facilitates fast manipulations on large amounts of business dataand/or replication of entire business suite information. Thus, in someembodiments, the in-memory database may provide for the following designprinciples/concepts: business data in real-time (e.g., GUI patterns forconstantly updated business data); well modeled tables and data cubes(e.g., in order to provide semantic services); a highly parallelizedcomputational engine (e.g., for computationally intensive GUI patternssuch as real time alerts and/or suggestions); close coupling of businesslogic and business data (e.g., eliminating indexing and caching).

The illustrated in-memory database 140 stores one or more data objects143. The data objects 143 may include and/or reference a variety ofobjects that store and/or include business data. For instance, the dataobjects 143 may be data cubes, such as OLAP (online analyticalprocessing) cubes. The data cubes may consist of a data structure thatallows for columnar data storage rather than, e.g., row data storage;different types of indices compared to relational databases; andin-memory technology as compared to data stored in relational databases.The data cube may also allow manipulation and/or analysis of the datastored in the cube from multiple perspectives, e.g., by dimensions,measures, and/or elements of the cube. A cube dimension defines acategory of data stored in the cube, for example, a time duration ofcertain business data, a product or service, business user roles, and avariety of other categories. In other words, a cube dimension may be oneway to slice business data stored in the cube according to some businesslogic (e.g., logic within and/or associated with the contextualworkspace modules). In some instances, the data cube may havethree-dimensions, but any number of dimensions may be designed into thecube (e.g., a hypercube).

A cube measure may be a fact, such as a numeric fact, that iscategorized into one or more dimensions. Measures may include, forexample, specific product sales data according to a set period of time.Measures may also include, for example, manufacturing efficiency datafor a particular organizational unit of a business enterprise. In short,measures may include any appropriate business data that may bemanipulated according to business logic to assist or support thebusiness enterprise.

One or more functions may be performed on a data cube. For instance, thedata cube may be pivoted, in various ways. Each pivot provides thebusiness user with a distinct view of particular business data stored inthe cube. For instance, in one view, a business user may be presentedwith sales data of a specific data within a particular geographic regionacross a particular time period with a particular focus on the sales vs.geography relationship. In another view, the same data (e.g., the samemeasures and elements) may be presented with a different focus, e.g.,the sales vs. time period relationship. In some aspects, pivoting a datacube in real-time may allow the business user to more efficientlyanalyze the business data.

Other functions performable on data cubes may be, for instance, slice,dice, drill down/up, and roll-up. A slice operation identifies a subsetof a multi-dimensional array corresponding to a single value for one ormore members of the cube dimensions not in the subset. A dice operationis a slice operation on more than two dimensions of a data cube (or morethan two consecutive slices). A drill down/up operation allows thebusiness user to navigate the data cube's levels of data to reveallevels containing the most summarized (up) data to the most detailed(down) data. A roll-up operation involves computing all of the datarelationships for one or more dimensions of the data cube.

The data objects 143 may include and/or store other forms of data alongwith or in place of data cubes. For example, the data objects 143 mayrepresent, store, and/or reference data from one or more contentsources, such as web content, feeds, REST services, business datarepositories, reports, status updates, discussions, wikis, blogs, andother content sources. Of course, while illustrated as contained in thein-memory database 140, the data objects 143 may also be stored, forexample, in one or both of the memories 155, in the remote computingsystem 130, and/or a separate repository communicably coupled to thenetwork 120. In some embodiments, the data objects 143 may be stored ina raw, compiled, or compressed form or combination thereof.

The illustrated client system 110 includes one or more client appliances125 having corresponding GUIs 127. Each client appliance 125 may be anycomputing device operable to connect to or communicate with at least theserver system 105 and/or via the network 120 using a wireline orwireless connection. Further, as illustrated, each client appliance 125includes a processor 150, an interface 145, and a memory 155. Ingeneral, each client appliance 125 comprises an electronic computerdevice operable to receive, transmit, process, and store any appropriatedata associated with the environment 100 of FIG. 1. It will beunderstood that there may be any number of client appliances 125associated with, or external to, environment 100. For example, whileillustrated environment 100 illustrates three client appliances,alternative implementations of environment 100 may include a singleclient appliance 125 communicably coupled to the server system 105, orany other number suitable to the purposes of the environment 100.

Additionally, there may also be one or more additional client appliances125 external to the illustrated portion of environment 100 that arecapable of interacting with the environment 100 via the network 120.Further, the term “client” and “user” may be used interchangeably asappropriate without departing from the scope of this disclosure.Moreover, while each client appliance 125 is described in terms of beingused by a single user, this disclosure contemplates that many users mayuse one computer, or that one user may use multiple computers. As usedin this disclosure, client appliance 125 is intended to encompass atablet computing device, personal computer, touch screen terminal,workstation, network computer, kiosk, wireless data port, smart phone,personal data assistant (PDA), one or more processors within these orother devices, or any other suitable processing device. For example,each client appliance 125 may comprise a computer that includes an inputdevice, such as a keypad, touch screen, mouse, or other device that canaccept user information, and an output device that conveys informationassociated with the operation of the server system 105 or the clientappliance 125 itself, including digital data, visual information, anyapplication, or the GUI 127. Both the input and output device mayinclude fixed or removable storage media such as a magnetic storagemedia, CD-ROM, or other suitable media to both receive input from andprovide output to users of the client appliances 125 through thedisplay, namely, the GUI 127.

The illustrated network 120 facilitates wireless or wirelinecommunications between the components of the environment 100 (i.e.,between the server system 105 and the client system 110), as well aswith any other local or remote computer (e.g., remote computing system130), such as additional clients, servers, or other devices communicablycoupled to network 120 but not illustrated in FIG. 1. The network 120 isillustrated as a single network in FIG. 1, but may be a continuous ordiscontinuous network without departing from the scope of thisdisclosure, so long as at least a portion of the network 120 mayfacilitate communications between senders and recipients. The network120 may be all or a portion of an enterprise or secured network, whilein another instance at least a portion of the network 120 may representa connection to the Internet. In some instances, a portion of thenetwork 120 may be a virtual private network (VPN), such as, forexample, the connection between the client system 110 and the serversystem 105.

Further, all or a portion of the network 120 can comprise either awireline or wireless link. Example wireless links may include802.11a/b/g/n, 802.20, WiMax, and/or any other appropriate wirelesslink. In other words, the network 120 encompasses any internal orexternal network, networks, sub-network, or combination thereof operableto facilitate communications between various computing components insideand outside the illustrated environment 100. The network 120 maycommunicate, for example, Internet Protocol (IP) packets, Frame Relayframes, Asynchronous Transfer Mode (ATM) cells, voice, video, data, andother suitable information between network addresses. The network 120may also include one or more local area networks (LANs), radio accessnetworks (RANs), metropolitan area networks (MANs), wide area networks(WANs), cellular networks, all or a portion of the Internet, and/or anyother communication system or systems at one or more locations.

The illustrated remote computing system 130 is communicably coupled toone or both of the server system 105 and client system 110 through thenetwork 120. In some instances, as illustrated, the remote computingsystem 130 stores and/or references third party content 135, such as,for example, data objects, web content, electronic communications,content feeds, and other data sources. Although illustrated as a singleappliance, the remote computing system 130 may include any number ofappliances (e.g., servers, clients, mobile devices, and otherwise)coupled to the network 120 individually and/or in groups. For instance,in some embodiments, the remote computing system 130 may be a webcontent server delivering web content to one or more of the clientappliances 125 in response to a request. In some embodiments, the remotecomputing system 130 may be a repository storing one or more dataobjects, such as data cubes or other form of database storing businessdata.

The illustrated communication interfaces 145 (shown as part of theserver appliance 115 and the client appliance 125) facilitatecommunication among appliances in, for example, the client system 110,the remote computing system 130, and the server system 105. Theinterfaces 145 may also facilitate communication among the illustratedsystems and other systems in a client-server or other distributedenvironment (including within environment 100) connected to the network.Generally, the interfaces 145 include logic encoded in software and/orhardware in a suitable combination and operable to communicate with thenetwork 120. More specifically, the interfaces 145 may include softwaresupporting one or more communication protocols associated withcommunications such that the network 120 or interface's hardware isoperable to communicate physical signals within and outside of theillustrated environment 100.

The illustrated processors 150 (shown as part of the server appliance115 and the client appliance 125) may be a central processing unit(CPU), a blade, an application specific integrated circuit (ASIC), afield-programmable gate array (FPGA), or another suitable component.Generally, the processors 150 execute instructions and manipulate datato perform the operations of the respective server appliance 115 andclient appliance 125 and, specifically, the contextual workspace module(server) 160 and, contextual workspace module (client) 165), and thegraphical exploration module 180, as well as any other applications.Specifically, the server appliance's processor 150 executes thefunctionality required to receive and respond to requests from theclient appliances 125 and their respective applications (e.g.,contextual workspace (client) 165), as well as the functionalityrequired to perform the other operations of the contextual workspacemodule (server) 160.

Regardless of the particular implementation, “software” may includecomputer-readable instructions, firmware, wired or programmed hardware,or any combination thereof on a tangible medium operable when executedto perform at least the processes and operations described herein.Indeed, each software component may be fully or partially written ordescribed in any appropriate computer language including C, C++, Java,Visual Basic, assembler, Perl, any suitable version of 4GL, as well asothers. It will be understood that while portions of the softwareillustrated in FIG. 1 are shown as individual modules that implement thevarious features and functionality through various objects, methods, orother processes, the software may instead include a number ofsub-modules, third party services, components, libraries, and such, asappropriate. Conversely, the features and functionality of variouscomponents can be combined into single components as appropriate.Although illustrated as a single processor 150 for each of therespective server appliance 115 and client appliance 125 in FIG. 1, twoor more processors may be used according to particular needs, desires,or particular embodiments of environment 100.

The illustrated memories 155 (shown as part of the server appliance 115and the client appliance 125) may include any memory or database moduleand may take the form of volatile or non-volatile memory including,without limitation, magnetic media, optical media, random access memory(RAM), read-only memory (ROM), removable media, or any other suitablelocal or remote memory component. Each memory 155 may store variousobjects or data, including classes, frameworks, applications, backupdata, business objects, jobs, web pages, web page templates, databasetables, repositories storing business and/or dynamic information, andany other appropriate information including any parameters, variables,algorithms, instructions, rules, constraints, or references theretoassociated with the purposes of the respective server appliance 115 andclient appliance 125. Additionally, each memory 155 may include anyother appropriate data, such as VPN applications, firmware logs andpolicies, firewall policies, a security or access log, print or otherreporting files, as well as others.

As illustrated, memory 155 of the server appliance 115 includes and/orstores one or more server content objects 170. Memory 155 of the clientappliance 125 includes and/or stores one or more client content objects175. In some embodiments, the server content objects 170 and/or theclient content objects 175 may be similar to the data objects 143 storedin the in-memory database 140. For example, the content objects 170 and175 may be data cubes, tables, reports, or other content sources, suchas web content, electronic communications, feeds, and otherwise.Regardless of the form of the content objects 170 and 175, these objectsmay contain and/or reference business data on which business logic maybe applied, e.g., by the contextual workspace module (server) 160 and/orcontextual workspace module (client) 165, in order to realize and/oraccomplish a task in a business environment.

The illustrated computing system 100 includes a contextual workspacemodule (server) 160 and a contextual workspace module (client) 165, anda graphical exploration module 180. At a high level, each of thecontextual workspace module (server) 160 and contextual workspace module(client) 165 (referred to collectively as the contextual workspacemodules), and the graphical exploration module 180, is any application,program, module, process, or other software that may execute, change,delete, generate, or otherwise manage information according to thepresent disclosure, particularly in response to and in connection withone or more requests received from the illustrated client appliances 125and their associated applications. In certain cases, only one contextualworkspace module (server) 160 may be located at a particular serverappliance 115. In others, a plurality of related and/or unrelatedcontextual workspace module (server) 160 may be stored at a singleserver appliance 115, or located across a plurality of other serverappliances 115 in the server system 105, as well.

In certain cases, the contextual workspace modules and the graphicalexploration module 180 may be implemented as composite applications. Forexample, portions of the composite application may be implemented asEnterprise Java Beans (EJBs) or design-time components may have theability to generate run-time implementations into different platforms,such as J2EE (Java 2 Platform, Enterprise Edition), ABAP (AdvancedBusiness Application Programming) objects, or Microsoft's .NET, amongothers. Additionally, the contextual workspace modules may representweb-based applications accessed and executed by remote client appliances125 or client applications via the network 120 (e.g., through theInternet).

Further, while illustrated as internal to server appliance 115, one ormore processes associated with the contextual workspace module (server)160 or the graphical exploration module 180 may be stored, referenced,or executed remotely. For example, a portion of the contextual workspacemodule (server) 160 may be a web service associated with the applicationthat is remotely called, while another portion of the contextualworkspace module (server) 160 may be an interface object or agentbundled for processing at a remote client appliance 125 via thecontextual workspace module (client) 165. Moreover, any or all of thecontextual workspace modules or the graphical exploration module 180 maybe a child or sub-module of another software module or enterpriseapplication (not illustrated) without departing from the scope of thisdisclosure.

As explained more fully below, one, all, or a combination of thecontextual workspace modules 160 and 165, and the graphical explorationmodule 180, may present or facilitate presentation of a user interface,e.g., through GUI 117 and/or GUI 127, to a business user to perform avariety of tasks. For instance, the contextual workspace modules 160 and165 may present one or more workspace modules containing business datarelevant to the business user in a variety of forms, e.g., tables,graphs, notes, and otherwise. The relevant business data may be sourcedfrom, for example, the data objects 143 or other content sources. Thegraphical exploration module 180 may present a graphical depiction ofrelationships between different data cubes or specific data objects,allowing a user to select particular elements of the graphical depictionfor generating new data cubes.

FIGS. 2A-2B illustrate additional example distributed computing systems200 and 250, respectively, operable to generate, view, modify, and/orotherwise manipulate a contextual workspace. Distributed computingsystems 200 and 250, in some embodiments, may illustrate two differentexample configurations for deployment of a contextual workspace within adistributed computing environment. For example, in some instances,either of the distributed computing systems 200 or 250 may represent analternate deployment of a contextual workspace within a distributedcomputing environment to the computing system 100 shown in FIG. 1. Theexample configurations for distributed computing systems 200 and 250 canalso be configured to provide for graphical exploration of an in-memorydatabase, as described below in connection with FIGS. 4A-D and FIG. 5.In particular, user interfaces provided in a contextual workspace can beenriched with new and alternative views on business data in real-time.

Turning to FIG. 2A, the illustrated distributed computing system 200includes a web portal 205 communicably coupled to a virtual appliance215 and an in-memory database 220. In some embodiments, the web portal205 provides an authentication portal allowing a single point of accessto applications, business data, and services both within and external toa business enterprise. For instance, an employee or user of the businessenterprise may access the virtual appliance 215 and/or the in-memorydatabase 220 via the web portal 205. In other words, the web portal 205may provide a front end accessible through a client appliance located atthe business enterprise.

The illustrated web portal 205 includes a client enterprise workspace210. In some aspects, the client enterprise workspace 210 may besubstantially similar to the contextual workspace module (client) 165shown in FIG. 1. For instance, the client enterprise workspace 210 maypresent or facilitate presentation of a user interface (e.g., one ormore workspace modules described more fully below) to the business userto perform a variety of tasks.

The illustrated virtual appliance 215 may be substantially similar tothe server appliance 115 illustrated in FIG. 1 and include a serverenterprise workspace 216, a business intelligence module 217, and anopen source module 218. The server enterprise workspace 216, in someimplementations, may be substantially similar to the contextualworkspace module (server) 160 shown in FIG. 1. For instance, the serverenterprise workspace 216 may provide for a customizable and dynamicvirtual workspace accessible to a client and/or user via a GUI, e.g.,through the web portal 205. In some aspects, as explained more fullybelow, such a virtual contextual workspace may provide for a number offunctional features and advantages, such as, for instance, providingsemantic context to relevant business data, providing suggestions to theuser to add semantic and/or social data, and generating a number ofranked suggestions to add semantic data to the workspace according tosocial criteria.

The illustrated business intelligence module 217, at a high level,provides software and/or middleware services for performance analytics(e.g., supports organizational efforts to develop sophisticated visualrepresentations of processes and performance, providing organizationswith new insights that can help them make more informed decisions,assess and plan a business intelligence strategy, deploy dashboardtools, generate management and operational reports, and build an ITinfrastructure that provides high scalability for users and data);services for analytic applications (e.g., provides guidance anddeployment expertise in implementing analytic applications, offeringpre-built analytics and data models to help a customer with a specificbusiness problem in various industries, helping organizations toefficiently deploy applications); and introductory business intelligenceservices (e.g., introduces organizations to the dynamics of usingbusiness intelligence, providing the ability to leverage thefunctionality of business intelligence—such as executive dashboards andoperational reports—without initiating a full-scale implementation).

The illustrated open source module 218, in some embodiments, may be anopen source web application framework, such as, for example, a Rails (orRoR) for the Ruby programming language. In some embodiments, the module218 may provide generic services like authentication, authorization,repository, logging, and otherwise

The illustrated virtual appliance 215 is communicably coupled to abusiness object repository 235 in order to consume (e.g., retrieve,modify, manipulate or otherwise) business objects relevant to thebusiness enterprise. Each business object stored on the repository 235,for example, a capsule with an internal hierarchical structure, behavioroffered by its operations, and integrity constraints. In general, theoverall structure of the business object model ensures the consistencyof the interfaces that are derived from the business object model. Thederivation helps ensure that the same business-related subject matter orconcept can be represented and structured in the same way in variousinterfaces. The business object model defines the business-relatedconcepts at a central location for a number of business transactions. Inother words, it reflects the decisions made about modeling the businessentities of the real world acting in business transactions acrossindustries and business areas. The business object model is defined bythe business objects and their relationship to each other (the overallnet structure).

Business objects are generally semantically disjointed, i.e., the samebusiness information is represented once. In some embodiments, thebusiness objects are arranged in an ordering framework such that theycan be arranged according to their existence dependency to each other.For example, in a modeling environment, the customizing elements mightbe arranged on the left side of the business object model, the strategicelements might be arranged in the center of the business object model,and the operative elements might be arranged on the right side of thebusiness object model. Similarly, the business objects can be arrangedin this model from the top to the bottom based on defined order of thebusiness areas, e.g., finance could be arranged at the top of thebusiness object model with customer relationship management (CRM) belowfinance and supplier relationship management (SRM) below CRM. To helpensure the consistency of interfaces, the business object model may bebuilt using standardized data types as well as packages to group relatedelements together, and package templates and entity templates to specifythe arrangement of packages and entities within the structure.

A business object may be defined such that it contains multiple layers.Typical business object may contains four layers: a kernel layer, anintegrity layer, an interface layer, and an access layer. The innermostlayer of the example business object is the kernel layer. The kernellayer represents the business object's inherent data, containing variousattributes of the defined business object. The second layer representsthe integrity layer. The integrity layer contains the business logic ofthe object. Such logic may include business rules for consistentembedding in a computing environment and the constraints regarding thevalues and domains that apply to the business object. Business logic maycomprise statements that define or constrain some aspect of thebusiness, such that they are intended to assert business structure or tocontrol or influence the behavior of the business entity. It may pertainto the facts recorded on data and constraints on changes to that data.In effect, business logic may determine what data may, or may not, berecorded in business object. The third layer, the interface layer, maysupply the valid options for accessing the business object and describethe implementation, structure, and interface of the business object tothe outside world. To do so, the interface layer may contain methods,input event controls, and output events. The fourth and outermost layerof the business object is the access layer. The access layer defines thetechnologies that may be used for external access to the businessobject's data. Some examples of allowed technologies may includeCOM/DCOM (Component Object Model/Distributed Component Object Model),CORBA (Common Object Request Broker Architecture), RFC (Remote FunctionCall), Hypertext Transfer Protocol (HTTP) and Java, among others.Additionally, business objects of this embodiment may implement standardobject-oriented technologies such as encapsulation, inheritance, and/orpolymorphism.

The illustrated system 200 includes the in-memory database 220communicably coupled to the web portal 205 and the virtual appliance215. For example, as illustrated, the in-memory database 220 may beinvoked by either or both of the web portal 205 and the virtualappliance 215. The in-memory database 220 may be substantially similarto the in-memory database 140 described in FIG. 1. Further, thein-memory database 220 includes in-memory business logic 225 (which, asillustrated, may be invoked by either or both of the web portal 205 andthe virtual appliance 215). The in-memory business logic 225 may includea combination of one or more of software, middleware, and hardware (orother logic) operable to manipulate one or more data objects (e.g.,stored in-memory on the database 220). For instance, the in-memorybusiness logic 225 may perform functionality associated with and/or usedby the enterprise workspace (client) 210 and/or enterprise workspace(server) 216, such as the methods described with reference to FIGS. 5-7and 9, as well as other functionality disclosed herein.

The illustrated in-memory database 220 is communicably coupled to abusiness suite 240. In some embodiments, as described above, complete orpartial processing may be done directly on a database, thus renderingmaking components like the “database,” “server,” and/or “suite”substantially similar or identical. At a high level, the business suite240 may consist of one or more integrated enterprise applicationsenabling, e.g., business enterprises, to execute and optimize businessand IT strategies, such as performing industry-specific, andbusiness-support processes. In some embodiments, the business suite 240may be built on an open, service-oriented architecture (SOA). Suchintegrated business applications may be replicated, for example, via theweb portal 205 through the in-memory database 220, such as via theenterprise workspace (client) 210.

The illustrated in-memory database 220 is also communicably coupled tothird party content 245. The third party content 245, for example, maybe substantially similar to data stored and/or referenced by the remotecomputing system 135 described with reference to FIG. 1, such as, forexample, the third party content 135. In some aspects, the third partycontent 245 may be stored and/or accessed through relational databases(i.e., relational databases stored on magnetic memory that require aprocess, e.g., ETL, to transfer data from one system to another not inreal time but with a delay of an hour, day, week, or longer). Forinstance, while some business data may be stored on the in memorydatabase 220 and therefore, be accessible in real-time, some businessdata may be available to and/or exposed by the in-memory business logic225, such as the third party content 245.

The illustrated system 200 also includes one or more clients 230. Forinstance, as illustrates, the clients 230 may include a mobile client231, and a web workspace 232. One or more of the clients 230 may invoke,for example, the enterprise workspace (server) 216, on the virtualappliance 215 in order to access and/or expose the functionality of thein-memory business logic 220. For example, the mobile client 231 mayfacilitate invocation of the enterprise workspace (server) 216 from amobile device (e.g., cell phone, smart phone, email mobile device, PDA,or other mobile device). The web workspace 232 may, for instance,facilitate invocation of the enterprise workspace (server) 216 through anetwork connection, such as the global network known as the world wideweb.

Turning to FIG. 2B, the illustrated distributed computing system 250includes the web portal 205 communicably coupled to an in-memoryapplication server 255, which, in turn, is communicably coupled to theclients 230, the business object repository 235, and a businessfinancial management module 260. Accordingly, as illustrated, thedistributed computing system 250 may provide for an alternativedeployment configuration of a system operable to generate, view, modify,and/or otherwise manipulate a contextual workspace, as compared to thesystem 200, but with similar (or identical) components in some respect.For example, as illustrated, the in-memory application server 255 insystem 250 may replace the virtual appliance 215/in-memory database 220shown in FIG. 1. The in-memory application server 255, as illustrated,includes the in-memory database 220 (including, for example, thein-memory business logic 225, not shown here). The in-memory applicationserver 255, as illustrated, also includes the server enterpriseworkspace 216, the business intelligence module 217, and the open sourcemodule 218.

The illustrated business financial management module 260, generally,supports IT services costing, customer value analysis, cost to serve,bill of material costing, and activity based costing, among otherfinancial metrics. In some implementations, for example, the businessfinancial management module 260 may provide for user-friendly, rapid,and efficient model building; accurate calculation and cross-charge forIT and other shared services; monitoring and manage costs, pricing, andtrue profitability; accurate assignment of overheads and indirect coststo customers, products, and channels; access to cost and profitabilitydata by customer, product, or business channel; accurate assignment,monitoring, and analysis of overhead costs tied to bill of materials,among other functions.

FIG. 3 illustrates an example virtual distributed computing architecture300 operable to generate, view, modify, and/or otherwise manipulate acontextual workspace. In some implementations, the example virtualdistributed computing architecture 300 is also operable to enrich a userinterface presented in the contextual workspace by generating graphicalrepresentation of relationships between data cubes or data objects thatwere not previously defined in existing data cubes, and generating newdata cubes at runtime based on user exploration of the graphicalrepresentations. As illustrated, the virtual distributed computingarchitecture 300 includes a web client 302, a web server 340, anin-memory computing engine 356, one or more external services 370, oneor more consumption channels 372, one or more external content providers374, and one or more data sources 376. As further illustrated, the webclient 302, the web server 340, the in-memory computing engine 356, theone or more external services 370, the one or more consumption channels372, the one or more external content providers 374, and/or the one ormore data sources 376 may be communicably coupled through an HTTP and/orREST architecture.

The illustrated web client 302 includes a UI module 304, a connectivitymodule 306, and a context module 308. The illustrated UI module 304, insome embodiments, may generate, modify, and/or maintain a virtualcontextual workspace and present the workspace to a user through a GUI.The UI module 304 includes a workspace manager 310, a module gallery312, a layout manager 314, a data orchestrator 316, a contextualassistance module 318, a global filtering module 322, a data streamingmodule 324, and a timeline module 326. The illustrated workspace manager310, in some embodiments, may include functionality that managesoperation and use of the contextual workspace by the user.

The illustrated module gallery 312, in some embodiments, may keep trackof one or more workspace modules (such as the workspace modules shown inFIGS. 4A-4C) viewable on and/or available to a contextual workspacepresented to the user. For example, the module gallery 312 may includean index (e.g., stored in a repository) of the current workspace modulesbeing viewed by the user in the contextual workspace.

The illustrated layout manager 314, in some embodiments, may determinehow one or more selected and/or requested workspace modules may bearranged on the contextual workspace. For example, the layout manager314 may determine a size and/or placement of each module on thecontextual workspace in order to, for instance, better manage thecontextual workspace in the GUI presented to the user.

The illustrated data orchestrator 316, in some embodiments, maydetermine, for example, one or more available views and/or functionalityavailable to the user with respect to a data cube (e.g., an OLAP cubestoring business data exposable to the user). For example, the dataorchestrator 316 may determine how to present or show any data model,including a data cube, to the user (e.g., icon, full screen, orsomething in between). In some embodiments, for example, the dataorchestrator 316 may function along with other components of theillustrated architecture (e.g., the workspace manager 310, the modulegallery 312, the layout manager 314, and other components) to performthe functionality described in FIGS. 4A-D and 5, as well as otherprocesses and methods described herein.

The illustrated contextual assistance module 318, in some embodiments,may include functionality that provides the user of the contextualworkspace generated by the architecture 300 with additional businessdata contextually relevant with business data being currently viewed inthe workspace. For instance, the contextual assistance module 318 (orother modules included in the illustrated architecture 300) may identifysemantically related information to that currently viewed on thecontextual workspace by the user. The semantically related informationmay be suggested to the user, for example, to add to the contextualworkspace through one or more workspace modules.

The illustrated global filtering module 320, in some embodiments, mayprovide for functionality that, for example, ranks and/or filtersvarious suggestions for content (e.g., semantically related content orsocially related content) to be added to the user's contextual workspacethrough architecture 300. For instance, the global filtering module 320(or other modules included in the illustrated architecture 300) may ranksuggestions to add semantically related data to the user's contextualworkspace according to one or more social metrics.

The illustrated data annotations module 322, in some embodiments, mayprovide for functionality that, for example, allows a user to generatenotes (or other annotations) that provide specific context to datawithin one or more workspace modules in the contextual workspace. Forinstance the data annotations module 322 may, in some aspects, allow theuser to dynamically create and/or view insights (e.g., notes or otherannotations) on contexts of data within the contextual workspace. Theinsights could be for the user or other users (e.g., a collaboration).

The illustrated data streaming module 324, in some embodiments, maymanage, direct, and/or control the transfer of data (e.g., from dataobjects, data cubes, documents, reports, feeds, and other data sources)to, for example, the web client 302. For instance, the data streamingmodule 324 may manage the speed at which data is transferred to the webclient 302 to, for example, expose on one or more workspace modules ofthe contextual workspace generated and presented to the user.

The illustrated timeline module 326, in some embodiments, may providefor functionality that allows a user to “time travel” through one ormore previous views of a contextual workspace. For instance, in someembodiments, each distinct contextual workspace (i.e., each view with adistinct set, combination, and/or number of workspace modules includedwithin the contextual workspace) may be time stamped with a particulartime (e.g., hour/minute/day, date, week, month, or other granularity).The time stamp may be part of the metadata of the contextual workspacegenerated by and presented to the user. In some aspects, the user may“time travel” back in time to view previous versions of the contextualworkspace according to the time stamp. Thus, not only the data containedin the contextual workspace, but the user's subjective view on the data(e.g., which workspace modules were selected) can be retrieved. The usermay filter the contextual workspace versions by the time stamp, dataexposed in the contextual workspaces, keywords, and/or other metrics.

The illustrated connectivity module 306 includes, in some embodiments,context sources module 328 and a content fetching module 330, and, insome aspects, may provide connection functionality for the web client302 to communicate with, for example, the web server 340, the in-memorycomputing engine 356, the data sources 376, and/or other contentrepositories or sources. The illustrated context sources module 328, insome embodiments, may provide for functionality to the web client 302 toconnect to various sources of contextually relevant (e.g., semanticallyrelevant) data related to one or more workspace modules in thecontextual workspace. For example, semantically relevant data may beexposed from, for example, data objects, data cubes, documents, searchfunctions, social data (e.g., people), and other sources.

The illustrated content fetching module 330, in some embodiments, mayprovide functionality to retrieve and/or gather data content from one ormore data sources, such as, for example, data objects, data cubes,documents, search functions, social data (e.g., people), and othersources.

The illustrated context module 308 includes, in some embodiments, acontext discovery module 332, a suggestions and rankings module 334, anevents and alerts module 336, and a context model 338. In some aspects,the context module 308 may provide functionality related to discoveringand/or suggesting semantically relevant data (e.g., from data objects,data cubes, and otherwise) related to data being viewed by the user onthe contextual workspace. The illustrated context discovery module 332,in some embodiments, may provide functionality related to searching forand/or exposing semantically relevant content related to data on thecontextual workspace, e.g., in one or more workspace modules. Forexample, semantically relevant data may include information related todata that the user works with or views in the contextual workspaceand/or arranges (according to his/her expertise) within the contextualworkspace. For instance, the arrangement of data (e.g., which workspacemodules are selected and/or viewed) may provide at least a portion ofthe overall context of the workspace apart from the data (numerical orotherwise). The semantically relevant data may be related, e.g.,according to keywords, metadata, cube dimensions, cube measures, cubeviews, table headings, etc., which may be unknown to the user (due to,for instance, the vast amount of data available to the user).

The illustrated suggestions and rankings module 334, in someembodiments, may include functionality that provides suggestions ofsemantically relevant data to the user to add to the contextualworkspace. The suggestions and rankings module 334 may also includefunctionality that ranks a number of suggestions of semanticallyrelevant data according to, for example, matching keywords, matchingmetadata, social metrics, and/or other criteria.

The illustrated events and alerts module 336, in some embodiments, mayinclude functionality that allows the user set an alert, e.g., at agiven point of time when a particular event is occurring. The user,therefore, may be reminded (at a later time) of how that particularproblem was resolved. In some aspects, the events and alerts module 336may also provide a user with notification (via one or more workspacemodules of the contextual workspace) of events and/or alerts based on,for instance, actions of other users with similar roles within thebusiness enterprise (e.g., within the same organization unit, or havingthe same title).

The illustrated context model 338, in some embodiments, may build and/orgenerate a model (e.g., a context bag) containing the discoveredsemantically relevant data related to the contextual workspace dataexposed in one or more workspace modules.

The illustrated web server 340 includes a workspace services module 342.The illustrated workspace services module 342 includes a contentprovider manager 344, a workspace data manager 346, a context datamanager 348, a contextual services manager 350, and a data acquisitionand modeling module 354. The illustrated content provider manager 344and the illustrated workspace data manager 346, in some embodiments, maydirect, control, and/or otherwise manage the content and data exposed onthe one or more workspace modules in the contextual workspace.

The illustrated context data manager 348, in some embodiments, maydirect, control, and/or otherwise manage semantically relevant content(e.g., determined according to, for example, metadata contained in theworkspace modules on the contextual workspace). In some embodiments, theillustrated contextual services manager 350 may include functionalitythat assists (at least partially) the context data manager 348 indirecting, controlling, and/or otherwise managing semantically relevant.

The illustrated data acquisition and modeling module 354, in someembodiments, may perform modeling functionality on business data to beexposed in one or more workspace modules in the contextual workspace.For instance, the data acquisition and modeling module 354 may modeldata objects, such as data cubes, as three-dimensional workspace modulesin the contextual workspace. In some implementations, the dataacquisition and modeling module 354 may render three-dimensionalgraphical views on data cubes and other data objects to allow graphicalexploration of business data in an in-memory database, as describedbelow in relation to FIGS. 4A-4D and FIG. 5.

The illustrated in-memory computing engine 356 includes a socialsuggestions module 358, a semantic suggestions module 360, an alertengine 362, a data annotations module 364, a context database 366, and abusiness suite database 368. In some embodiments, the social suggestionsmodule 358 may, in some embodiments, include functionality that providessuggestions of socially relevant data to the user to add to thecontextual workspace. For instance, the suggestions may include businessdata that other users (e.g., other business users in the sameorganizational unit of the enterprise, same team of the user, or samerole of the user, to name a few) found relevant and/or useful. Thesuggested business data may also be contextually relevant to one or moreworkspace modules active in the user's contextual workspace.

The illustrated semantic suggestions module 360, in some embodiments,include functionality that provides suggestions of semantically relevantdata to the user to add to the contextual workspace. For instance, thesuggestions may include business data that has some related contextualsimilarity with data exposed in one or more workspace modules in theuser's contextual workspace. For example, the semantically relevant datamay be business data from a data cube having one or more similardimensions and/or measures as compared to a data cube currently beingviewed and/or analyzed in the contextual workspace. As another example,the semantically relevant data may be business data from a table havinga similar heading to a table currently being viewed in the contextualworkspace. Thus, the suggested semantically relevant data may bebusiness data that provides insight to the user that the business userwould not have known about otherwise.

The illustrated alert engine 362, in some embodiments, in someembodiments, may include functionality that allows the user set analert, e.g., at a given point of time when a particular event isoccurring. The user, therefore, may be reminded (at a later time) of howthat particular problem was resolved. In some aspects, the alert engine362 may also provide a user with notification (via one or more workspacemodules of the contextual workspace) of alerts based on, for instance,actions of other users with similar roles within the business enterprise(e.g., within the same organization unit, or having the same title).

The illustrated data annotations module 364, in some embodiments, mayprovide for functionality that, for example, allows a user to generatenotes (or other annotations) that provide specific context to datawithin one or more workspace modules in the contextual workspace. Forinstance the data annotations module 364 may, in some aspects, allow theuser to dynamically create and/or view insights (e.g., notes or otherannotations) on contexts of data within the contextual workspace. Theinsights could be for the user or other users (e.g., a collaboration).

The illustrated context database 366, in some embodiments, may storeand/or reference one or more contextually relevant data objects, such asdata objects that have been determined (e.g., by one or more componentsof the architecture 300 such as the social suggestions module 358, thesemantic suggestions module 360, or other component) to be sociallyand/or semantically relevant based on one or more workspace modules inthe contextual workspace. For example, the context database 366 maystore and/or reference one or more data cubes, reports, documents,business objects, or other data objects. In some embodiments, thecontext database 366 may also store and/or reference one or moresuggestions (e.g., social, semantic, or otherwise) generated by thearchitecture 300.

The illustrated business suite database 368, in some embodiments, maystore and/or reference business data, such as business data utilized bythe business suite app 374 a.

The illustrated external services 370 include an event insight 370 a,social intelligence 370 b, text analysis 370 c, and feed services 370 d.Generally, the illustrated external services 370 may provide businessdata (e.g., semantically and/or socially relevant business data) to atleast one of the web client 302, the web server 340, and/or in-memorycomputing engine 356. For instance, the illustrated event insight 370 a,in some embodiments, may provide alerts to the web client 302 and/or webserver 340 to present to the business user through one or more workspacemodules of the contextual workspace. The illustrated social intelligence370 b, in some embodiments, may provide social business data (e.g.,social connections between one or more business users in a businessenterprise) to the web client 302 and/or web server 340 to present tothe business user through one or more workspace modules of thecontextual workspace. For example, the social business data may be agraph and/or chart showing business connections between employees, suchas, for instance, a degree of connection between the employees (e.g.,through one or more known business contacts). The illustrated textanalysis 370 c, in some embodiments, may identify business data locatedin documents or other data objects through, for instance, a keywordsearch or metadata search of the text in the documents. The illustratedfeed services 370 d, in some embodiments, may provide business datathrough one or more web feeds to the web client 302 and/or web server340 (e.g., RSS feeds).

The illustrated consumption channels 372 include a mobile channel 372 aand one or more feed clients 372 c. Typically, the consumption channels372 may also provide business data (e.g., semantically and/or sociallyrelevant business data) to at least one of the web client 302, the webserver 340, and/or in-memory computing engine 356. For instance, theillustrated mobile channel 372 a, in some embodiments, may providebusiness to the web client 302 and/or web server 340 from one or moremobile channels to present to the business user through one or moreworkspace modules of the contextual workspace.

The illustrated feed clients 372 c, in some embodiments, may include,for example, a mail client, which may provide business data to at leastone of the web client 302, the web server 340, and/or in-memorycomputing engine 356 through one or more electronic communications.

The illustrated external content providers 374 include one or more UIapplications, such as a business suite app 374 a, a businessintelligence app 374 b, and a social gadget app 374 c. Typically, theexternal content providers 374 provide for a number of UI applicationsthat may be interfaced by a user, e.g., through the web client 302 orotherwise. The business suite app 374 a, for example, may be businessapplications that provide integration of information and processes,collaboration, industry-specific functionality, and scalability. Forinstance, the business suite app 374 a may include one or moreconstituents, such as CRM (Customer Relationship Management), ERP(Enterprise Resource Planning), PLM (Product Lifecycle Management), SCM(Supply Chain Management), and/or SRM (Supplier RelationshipManagement). The business intelligence app 374 b, for example, mayprovide for a UI application that may be used in identifying andextracting business data (e.g., ETL) and analyzing such business data.The business data may include, for example, sales revenue, profits,and/or costs broken down by products and/or departments. In someaspects, the business intelligence app 374 b may provide for historical,current, and/or predictive views of business operations, such asreporting, online analytical processing, analytics, data mining, processmining, business performance management, benchmarking, text mining,and/or predictive analytics. In some aspects, the business intelligenceapp 374 b may also provide for layers including an ETL layer (e.g.,responsible for extracting data from a specific source, applyingtransformation rules, and loading it into a data warehouse); the datawarehouse (e.g., responsible for storing the information in varioustypes of structures); reporting (e.g., responsible for accessing theinformation in the data warehouse and presenting it in a user-friendlymanner to the business user); and a planning and analysis layer (e.g.,provides capabilities for the user to run simulations and perform taskssuch as budget calculations). The illustrated social gadget app 374 c,for example, may include games, social news feeds and other UI apps.

The illustrated data sources 376 include an in-memory database 376 a, agateway 376 b, one or more feeds 376 c, and one or more documents 376 d.Typically, the data sources 376 may provide data, such as business data,to the illustrated web client 302 and/or web server 340 that may bemanipulated to, for instance, provide suggestions of additionalcontextual data, provide suggestions of semantically-related data,and/or provide other suggestions, alerts, or events. For example, thein-memory database 376 a may store and/or reference one or more datacubes containing business data. The gateway 376 b may, for example, be afront-end server to the business suite app 374 and expose business suiteservices in an open protocol format. The feeds 376 c, for instance, mayprovide business and/or social data through social updates, for example,Twitter, Facebook, RSS feed/channels, and/or other feed sources orchannels. The documents 376 d, for instance, may be any number ofdocuments stored in or referenced by a document store (i.e., in arelational database or otherwise).

FIGS. 4A-4D illustrate example screenshots 400 a, 400 b, 400 c, and 400d, of an application providing graphical exploration of data objects inan in-memory database. Turning to FIG. 4A, an existing data cube isillustrated. The data cube may be presented in a contextual workspace,for example. In an initial stage, a user is provided access topredefined data cubes. The user is presented with data in a particularview as defined by the predefined data cubes, allowing the user tointeract with (e.g., select, modify, slice, dice, pivot, or otherwise) aparticular measure, dimension, and/or view of the data cube. Theinteraction by the user may be an interaction with at least some of thedata contained in the data cube exposed in one or more workspace moduleswithin the contextual workspace. For example, as depicted in FIG. 4A, avisual representation of a sales data cube 412 is presented in the upperhalf 410 of the screen shot 400 a and an analysis tool 420 is presentedin the lower half. The sales data cube 412 is already modeled usingcertain measures and dimensions in a predefined manner. The analysistool 420 depicts classical point-of-sales data in the view provided bythe sales data cube 412. For example, the point-of-sales data caninclude revenue sales data arranged by quarter 424, state 426, city 428,or lines of sales 429. In some implementations, classical point-of-salesdata can include a view of sales organized by margin, quantity sold, ornumber of occurrences, as illustrated in the “Measures” window 422 inFIG. 4A.

In certain instances, however, a user may need to query the in-memorydatabase for data objects or views that may not match the predefineddata cubes. The user may be limited, at least to some extent, toexploration of the predefined sales data cube and not be able to viewnon-classical point-of-sales data in the view provided by the sales datacube. For example, classical point-of-sales data may not include a viewof sales data based on marital status of the buyer or employee.Accordingly, a user is not able to analyze sales data in terms ofmarital status given only the view presented in the predefined salesdata cube 412.

FIG. 4B depicts a screenshot 400 b of a data graph 414 generated inresponse to a query for a particular search term 402. A query can beexecuted for a search term 402 to obtain relevant data connected to thecurrent data cube 412. In the illustrated example, if a user desires tocheck a non-classical point-of-sales data category, such as maritalstatus, the user can search for the term “marital status” 402 in asearch field. In some implementations, a drag-and-drop operation can beperformed to initiate a search based on a suggested measure in thecurrent data cube. The search term 402 may not be associated with datacurrently displayed in connection with the current data cube 412. Thequery is executed across the in-memory database while searchingdimensions and measures of existing data objects. The search may not belimited to a full text search and can include semantic searches.

In certain instances, the search results include or are associated withother data cubes 417 and 418 that are responsive to the search term 402.The search results can be one or more data objects 415 and 416 that arenot included in the initial, predefined data cube. The data objects 415and 416 returned from the search may be represented as nodes in the datagraph 414 displayed in the application. In some instances, the dataobjects 415 and 416 are also associated with different data cubes 417and 418. As seen in FIG. 4B, a visual representation of the data cubes417 and 418 associated with the responsive search results can bedisplayed, along with a graphical relation of the original data cube,the sales data cube 412 in the present example, to the data cubes 417and 418 returned from the search. The search for “marital status” canreturn results associated with an employee marital status 415 and abuyer marital status 416, for example. The paths from the sales datacube 412, and any related or intervening nodes, can be traced from thesearch results back to the initial data cube 412 and displayed to theuser. In some implementations, the shortest path between the sales datacube 412 and the nodes associated with the search results is determined.Accordingly, the user can browse the displayed graph and explore newpaths to different data cubes. In the illustrated example, a connectingnode can include a human resources data cube 417 between the sales datacube 412 and the employee marital status data objects 415, and acustomer relationship management (CRM) data cube 418 between the salesdata cube 412 and the buyer marital status data objects 416. The usercan then easily navigate the various nodes and data cubes associatedwith the marital status query.

FIG. 4C illustrates a screenshot 400 c depicting a user's selection of aparticular node associated with a data cube different from the initialsales data cube 412. In the present example, the buyer marital statusdata objects 416, illustrated as nodes in the data graph 414, associatedwith the CRM data cube 418 are selected by the user. In response toselection of the buyer marital status data objects 416, a new data cubeis generated that includes at least a dimension depicting a relationshipof the buyer marital status data objects 416 and other sales data.

For example, turning briefly to FIG. 6, an example algorithm 600 forcalculating a new data cube based on a predefined dimension. Algorithm600 may be implemented, for instance, to generate a new data cube thatincludes at least a dimension depicting a relationship of the buyermarital status data objects 416 and other sales data. In the examplealgorithm 600, “X” is given as a data cube selected from search resultson a plurality of data cubes for a particular dimension “x′” shown inthe algorithm 600. Further, in algorithm 600, “A” is given as an “entrypoint” data cube (e.g., a data cube that a user investigates and desiresto enrich with x′). In other words, A is the data cube to be generatedthat includes the particular dimension x′. The resultant execution ofalgorithm 600 generates the entry point data cube, A, with one or moreassumptions. For example, it is assumed that the shortest path from X toA is given as a graph having vertices that represent a plurality of datacubes and edges represent relationships between the data cubes. Further,the algorithm 600 follows a contiguous path in the graph from X to A(e.g., the algorithm asks a vertex in the graph about its next edge, andasks an edge about the next vertices in the path leading from X to A).

Returning to FIG. 4C, the analysis tool 420 automatically regeneratesthe presented view based on the data from the new data cube. In theillustrated example, sales revenue is arranged based on marital status,as indicated in the “Marital Status” window 430 of the analysis tool420.

FIG. 4D illustrates a screenshot 400 d of selection of another dimensionfrom the search results in addition to the previously selecteddimensions. Here, the user has selected employee marital status dataobjects 415 from the presented graphical paths in FIG. 4D. Based on theselection, another new data cube can be generated that includes salesdata in relation to employee marital status. The view presented in theanalysis tool 420 is also updated to reflect user selection of the newlygenerated data cube. Accordingly, certain data objects responsive to theuser's queries, and the relationships between the data objects and aninitial data cube, can be represented graphically as paths connected tothe initial data cube. New data cubes that include views of data notfound in previously defined data cubes can then be generated inreal-time based on user analysis of the graphical paths and selection ofcertain nodes in the paths.

In some implementations, a learning mechanism can be used to incorporatecommonly used search terms into standard data cubes. For example, as aparticular search term increases in use by one or more users, the searchterm or the results associated with querying the in-memory database withthe search term can be used to define additional data cubes. New datacubes can be automatically defined to include views of commonly analyzedcategories of data. Further, commonly analyzed data cubes, views, orsearch terms may be presented as semantic or social suggestions to auser in certain contexts. In certain instances, a user may be viewing aparticular data cube and is presented with suggestions for additionalviews on the underlying business data. The suggestions may be based onsearch terms that have been frequently used to identify additional viewswhen other users have viewed the same data cube, even if the suggestedviews are not directly connected to that data cube.

FIG. 5 illustrates an example process 500 for providing graphicalexploration of an in-memory database. First, a selected data cube isidentified at 502. The data cube may be identified via receiving aselection from a user. For example, a user may need a particular view onbusiness data and choose the particular view from a plurality ofdifferent, predefined data cubes. In some instances, the available,predefined data cubes or the selected data cube may not capture certainelements or non-classical dimensions that the user would like toanalyze. Accordingly, the user can enter a search term to identify dataobjects or relationships between data objects that the user may want toanalyze that are not currently represented in a predefined data cube.The in-memory database is searched for at least one data objectassociated with the search term at 504.

A relationship between the at least one data object and the selecteddata cube is determined at 506. In some instances, the relationship canbe represented as a path from the selected data cube to the data object.For example, the data object may be associated with a different datacube, and the different data cube can be traced back to the selecteddata cube directly or through other intervening data cubes, nodes, ordata objects. Further, multiple data objects can be identified from thesearch of the in-memory database. Each of the data objects may berelated to the selected data cube along a different path or along thesame path as other data objects.

The relationship is displayed as at least one path between the dataobject and the selected data cube at 508. The displayed path can includea display of each node or data cube needed to display the connectionbetween the data object and the selected data cube. Accordingly, a useris presented with a graphical depiction of relationships between aninitial view on business data and other data objects in an in-memorydatabase that the user has searched for. The user can explorerelationships within the in-memory database through the graphicalrepresentation to identify other dimensions to include in a new datacube. In some implementations, the user can select a particular node inthe graphical representation. Accordingly, a selection of a particularnode on the displayed path is received at 510.

The user's selection indicates a particular attribute or dimension thatwill be used to generate a new data cube separate from the selected datacube and other predefined data cubes. In response to the user'sselection, a new data cube is generated based on the selection at 512.The new data cube represents a new view on the underlying business datathat includes features that the user desires to explore and analyze thatmay not be found in any predefined data cube. Further, the search forrelevant data objects is performed on business data in the in-memorydatabase. Accordingly, generation of the new data cube can be performedin real-time as the user interacts with data cubes at runtime.

A number of implementations have been described. Nevertheless, it willbe understood that various modifications may be made. For example, othermethods described herein besides those or in addition to thoseillustrated in FIG. 5 may be performed. Further, the illustrated stepsof method 500 may be performed in a different order, either concurrentlyor serially. Further, steps may be performed in addition to thoseillustrated in method 500, and some steps illustrated in method 500 maybe omitted without deviating from the present disclosure. Further,algorithm 600 represents one example algorithm for calculating a newdata cube based on a predefined dimension and other algorithms may beused without departing from the scope of this disclosure or claims.Accordingly, other implementations are within the scope of the followingclaims.

1. A computer-implemented method performed with a computing system forgraphically exploring an in-memory database, the method comprising:identifying a first data object; searching an in-memory database for adata element associated with a search term, the in-memory databaseincluding the first data object and a second data object; determiningthat the data element is associated with the second data object;determining a first relationship between the first data object and thesecond data object, and a second relationship between the second dataobject and the data element; and generating a displayable graph of thefirst and the second relationship as a path between the data element andthe first data object, the path comprising at least three nodes, where afirst node represents the first data object, a second node representsthe data element, and a third node represents the second data object. 2.The computer-implemented method of claim 1, further comprising:receiving a selection of a particular node on the path; and generating anew data object based on the selection.
 3. The computer-implementedmethod of claim 2, wherein the new data object includes a view of datain the in-memory database different from a view associated with thefirst data object.
 4. The computer-implemented method of claim 2,wherein the new data object includes the particular node on the path. 5.The computer-implemented method of claim 1, wherein a plurality of dataobjects associated with the search term are identified in response tosearching the in-memory database.
 6. The computer-implemented method ofclaim 5, wherein a plurality of relationships are determined between thefirst data object and the plurality of data objects, wherein each of theplurality of relationships is displayed in at least one of a pluralityof paths, each of the plurality of paths sharing the first data objectas a common node.
 7. The computer-implemented method of claim 1, whereinthe search term is not associated with a dimension of the first dataobject.
 8. The computer-implemented method of claim 1, wherein the thirdnode is an intervening node between the first and second nodes. 9.(canceled)
 10. The computer-implemented method of claim 1, wherein thefirst data object is a data cube.
 11. An apparatus comprisinginstructions embodied on a tangible, non-transitory computer-readablemedia, the instructions operable when executed to cause a computingsystem to perform operations comprising: identifying a first dataobject; searching an in-memory database for a data element associatedwith a search term, the in-memory database including the first dataobject and a second data object; determining that the data element isassociated with the second data object; determining a first relationshipbetween the first data object and the second data object, and a secondrelationship between the second data object and the data element; anddisplaying the first and the second relationship as a path between thedata element and the first data object, the path including at leastthree nodes, wherein a first node represents the first data object, asecond node represents the data element, and a third node represents thesecond data object.
 12. The apparatus of claim 11, wherein theoperations further comprise: receiving a selection of a particular nodeon the path; and generating a new data object based on the selection.13. The apparatus of claim 12, wherein the new data object includes aview of data in the in-memory database different from a view associatedwith the first data object.
 14. The apparatus of claim 12, wherein thenew data object includes the particular node on the path.
 15. Theapparatus of claim 11, wherein a plurality of data objects associatedwith the search term are identified in response to searching thein-memory database.
 16. The apparatus of claim 15, wherein a pluralityof relationships are determined between the first data object and theplurality of data objects, wherein each of the plurality ofrelationships is displayed in at least one of a plurality of paths, eachof the plurality of paths sharing the first data object as a commonnode.
 17. The apparatus of claim 11, wherein the search term is notassociated with a dimension of the first data object.
 18. The apparatusof claim 11, wherein the third node comprises an intervening nodebetween the first and second nodes.
 19. (canceled)
 20. A computingsystem, comprising one or more memory modules, one or more processors,and instructions stored on one or more of the memory modules andoperable when executed with the one or more processors to performoperations comprising: identifying a first data object; searching anin-memory database for a data element associated with a search term, thein-memory database including the first data object and a second dataobject; determining that the data element is associated with the seconddata object; determining a first relationship between the first dataobject and the second data object, and a second relationship between thesecond data object and the data element; and displaying the first andthe second relationship as a path between the data element and the firstdata object, the path including at least three nodes, wherein a firstnode represents the first data object, a second node represents the dataelement, and a third node represents the second data object.
 21. Thecomputing system of claim 20, wherein the operations further comprise:receiving a selection of a particular node on the path; and generating anew data object based on the selection.
 22. The computing system ofclaim 21, wherein the new data object includes a view of data in thein-memory database different from a view associated with the first dataobject.
 23. The computing system of claim 21, wherein the new dataobject includes the particular node on the path.
 24. The computingsystem of claim 20, wherein a plurality of data objects associated withthe search term are identified in response to searching the in-memorydatabase.
 25. The computing system of claim 24, wherein a plurality ofrelationships are determined between the first data object and theplurality of data objects, wherein each of the plurality ofrelationships is displayed in at least one of a plurality of paths, eachof the plurality of paths sharing the first data object as a commonnode.
 26. The computing system of claim 20, wherein the search term isnot associated with a dimension of the first data object.
 27. Thecomputing system of claim 20, wherein the third node is an interveningnode between the first and second nodes.
 28. (canceled)
 29. Thecomputer-implemented method of claim 1, wherein determining that theparticular data element is associated with the second data objectfurther comprises determining that the particular data element is onlyassociated with the second data object.